Chapter 9 - Customer Relationship Management and Business Intelligence

1. What is your understanding of CRM?

CRM is the management of all aspects of customer relationships within an organisation. It is the intention of retaining good customers and allows profits to be decreased. Through customer relationship management it is less in cost to retain customers rather than searching for newer customers.


An example of Customer Relationship Management within an organisation.

2. Compare operational and analytical customer relationship management:




3.Describe and differentiate CRM technologies used by marketing departments and sales departments:

With regards to sales departments they computerise sales process and help sales people direct their jobs for example calendars, appointments, meetings and presentations. Sales departments tend to be more interested in contact management. Marketing departments tend to focus on campaign management; they view costs with a campaign, who their target market is and whether they are able to decide a return on investment for various campaigns.

4. How could a sales department use operational CRM technologies?

They could do this through using list generators. List generators are able to provide information on precise features of an organisation e.g. a marketing campaign would have list of customers in a geographic area and it can to arrange the lists of each customer in an area. Other strategies include campaign definition, planning and systems and cost sell or up sell.

5. Explain the problem associated with business intelligence. Describe the solution to this business problem:

If there is too much data an organisation may have limited knowledge on where data is or who their competitors may be.Hence may not be unable to make the best strategic implementation due to insufficient tools to back up data and support decision making to their strategic goals. The solution approach that they could take is business intelligence where it helps an organisation make decisions. Functional areas of an organisation can make decisions where they can be able to see more data analysis and reduce the latency of information in making good decisions.

6. Describe business intelligence and its value to businesses:

Business Intelligence refers to the tools and analysis that provide access to data for strategic decision making in an organisation for example data miming. Business intelligence has a value to an organisation in that it can be a whole myriad of things and can be used for many different aspects such as PivotTable on Microsoft Access. Business Intelligence is used for any tood that gives a longer length assortment of decision making and encourages types of decision making such as patterns and trends.





7. What are two possible outcomes a company could get from using data miming?

The two possible outcomes a company could get from data miming is that it can increase profits and allow an organisation to have better sales and confined resources. Another two outcomes include cluster and statistical analysis. Cluster analysis is the searching of equal or non equal but alike data in the many types of databases. However to undertake the cluster analysis, behavioural traits need to be clearly identified through physically partitioning the individual information into set groups. Statistical analysis assesses the data trends through the assistance of a regression analysis and statistics. Statistical analysis gives opportunity to workers in an organisation a large variety of statistical capabilities in order to construct things such as statistical models, the examination of model’s assumptions and validity and the comparison and contrasting the number of models to identify which is the better alternative for an issue within an organisation.


An example of a Cluster Analysis
http://www.blogger.com/post-edit.g?blogID=8243747997517258236&postID=89069071603267511


Baltzan, Phillips, Lynch, Blakey. 2010. Business Driven Information Systems. McGraw Hill. Sydney, Australia.

ETL - Tools Info. 2006-2010. Business Intelligence - Data Warehouse. http://etl-tools.info/en/bi/datawarehouse_concepts.htm. Date Accessed: 30 May 2010.

Execution - M.H. 2010. Data Miming Techniques - Propensity Modelling (Cluster Analysis). http://www.executionmih.com/data-mining/propensity-cluster-association-patterns.php Date Accessed: 30 May 2010.

SFGTV's Channel. 2007. YouTube. http://www.youtube.com/watch?v=6QvzxFJ_wc8. Date Accessed: 30 May 2010.